Intuition Crypto: Building The Internet’s Trust Layer

This case study explores how Intuition reframes a decentralized trust fabric for online knowledge, reputation, and discovery by aligning incentives across people, apps, and artificial intelligence. 

Prepared by Delphi Consulting as part of a broader engagement with the project team.

Artificial intelligence-generated summaries remain experimental; share concerns if you encounter issues.

1. The Web’s Trust Deficit

• Dominant platforms centralize discovery, shaping narratives and access to information.
• Artificial intelligence-first search will compress viewpoints into single answers, intensifying control.
• Crypto solved trustless settlement rails but has yet to decentralize discovery.

2. Beyond Binary Prediction Markets

• Markets like Polymarket reward accuracy but flatten complex issues into yes/no outcomes.
• This approach enables multi-context information markets where communities express nuanced stances.
• Trust staking captures confidence and context so multiple interpretations can coexist and accrue value.

3. Core Data Primitives

• Atoms: unique, portable identifiers for people, entities, concepts, and ideas.
• Triples: semantic links between Atoms that record truth claims and relevance.
• Together they form a Token Curated Graph, a decentralized semantic knowledge base.

4. Signal Economics

• Staking Trust on Atoms and Triples ranks relevance and credibility via bonding curves.
• Continuous curation drives canonical identifiers and credible statements to the top.
• Contributors earn fractional ownership in the knowledge they create or curate.

5. Incentives and Reputation

• Participants build knowledge portfolios and monetize high-signal contributions.
• Developers align with protocol growth by holding and delegating Trust.
• Trust circles enable contextual reputation that travels across applications and agents.

6. Developer and User Experience

• Builders skip custom databases and auth, composing directly on a shared data layer.
• Users gain data sovereignty: portable identity, preferences, and social graphs across Web3 apps.
• Discovery evolves from siloed feeds to an interoperable ecosystem.

7. Validation to Date

The project extends crypto’s mission from trustless money to trustful discovery. By making trust observable, quantifiable, and tradable, it turns information into an asset owned by its contributors and widely reusable by apps and agents.

The internet’s fracture isn’t about moving assets; it’s about judging claims, identities, and relationships. Today, your view of the world is mediated by closed platforms whose black-box algorithms decide which voices you hear. As artificial intelligence-driven search overtakes traditional methods by 2028, a few models will determine “truth,” compressing nuance into homogenized outputs.

In psychology and philosophy, intuition is often discussed in three broad forms: instinctive intuition (fast, gut-level judgments), experiential intuition (pattern recognition built through repeated exposure), and reflective intuition (sudden insight that can follow deliberate thought). In this report, “Intuition” refers to the protocol, not these cognitive categories.

Meanwhile, crypto perfected settlement at scale—DeFi, NFTs, and programmable money rails—yet left discovery under centralized control. To truly decentralize the web, we must apply the same rigor to how information is found, structured, and evaluated.

This protocol proposes a public, portable trust layer for interoperable knowledge and reputation—delivering a stark alternative to Web2’s walled gardens.

Decentralized trust layers make credibility portable by letting communities and applications share verifiable signals without handing control to a single platform.

The Rise of Prediction Markets

Prediction markets introduced incentive-aligned truth seeking, updating prices as new data appeared. Yet public debates are rarely binary. Complex topics demand context, values, and domain expertise that simple yes/no resolution cannot capture.

Consider a market on whether the United States will pass an artificial intelligence safety bill in 2025. Founders may fear stifled innovation, ethicists may emphasize alignment risks, and policymakers may focus on enforcement. Reducing this field of perspectives to a binary outcome loses the signal embedded in context.

Here, users stake Trust on relevance and accuracy rather than buying binary shares. Confidence and context are expressed along a curve, allowing diverse views to coexist and earn over time as evidence changes. The result is an information market that rewards nuance.

Binary markets showcase incentive power but stop short of composability, portability, and multi-perspective curation. This system preserves incentives while enabling reusable data and community-specific contexts—an upgrade akin to DeFi’s modularity, but for knowledge.

A Shift From Prediction to Proof

Token-curated registries demonstrated how game theory can coordinate shared lists; applying similar mechanics to information lets communities converge on canonical, decentralized identifiers for people, places, and abstractions. The aim parallels a universal token standard’s effect on tokens: a universal standard for data that composes cleanly across apps and chains.

By standardizing the “state of the state” above the ledger, developers can choose, fork, or extend schemas while incentives nudge convergence on the most useful, widely adopted structures.

At its core, the system operates as an open, verifiable, decentralized social and knowledge graph. It channels the crowd’s curation to assemble a permissionless map of entities and relationships.

Inspired by the Semantic Web and reimagined for Web3, it makes discrete knowledge ownable and monetizable. Two primitives power the model: Atoms and Triples.

From a user perspective, the flow is simple: create or find an Atom for an entity; add a Triple that links Atoms into a claim; stake the Trust token on the Atom or Triple to signal relevance (and, for Triples, perceived truth); earn a share of fees and ownership as others reuse and curate the same data; and use trust circles to filter discovery by the people and contexts you value.

Atoms

Atoms are unique, persistent identifiers for concepts, entities, and ideas—each with a decentralized identifier and immutable reference data. Markets, not gatekeepers, determine the canonical Atom for “Ethereum,” for example, enabling global referential consistency across apps and wallets.

Atom uniqueness is enforced via a hash of its underlying data, and permissionless creation invites market-driven convergence on standards.

• Use Atoms to reference the same data everywhere across crypto and the web.
• Accrue “equity” in Atoms by staking on their relevance and quality.
• Earn rewards tied to ongoing usage and curation.

These signals form a Token Curated Graph, within which Atom registries rank identifiers by relevance and stake, similar to a trending feed that reflects trading volume of attention and liquidity of conviction.

Over time, the most staked Atom becomes the canonical reference for a concept, simplifying developer integration and discovery.

Triples

Triples connect Atoms using subject–predicate–object statements (for example: “The New York Times” — “is” — “a reliable news source”). Triples can themselves be treated as Atoms in higher-order statements, enabling nested, expressive semantics.

Participants signal both truthfulness and relevance on Triples, acknowledging that a claim can be accurate yet unimportant to the market’s current needs. Economic pressure helps prune noise and elevate signal.

Triples accrue ownership stakes and fee flows, turning statements into living assets. Shared schemas emerge for common actions (follow, like, tag), allowing features to interoperate across apps without reinventing protocols.

Token Curated Graph

Atoms and Triples combine into a Token Curated Graph—an adaptable, decentralized semantic layer where communities weigh relevance, credibility, and context to build a web of trust richer than any single, imposed reality.

Signal economics orchestrate curation. Bonding curves on Atoms capture relevance, while curves on Triples map both relevance and truth signals. Staking Trust expresses conviction with economic weight.

To stake and earn rewards, a user connects a wallet in a supported app, acquires the Trust token, selects an Atom or Triple, and stakes an amount to express conviction. Rewards are not a fixed yield: they vary with usage-driven fees, how much others curate the same items, and whether a Triple resolves in your favor.

As more users stake on an Atom, its perceived importance climbs, accelerating discovery of canonical identifiers.

This flywheel concentrates stake on the highest-signal Atoms, guiding consensus about what to reference.

For Triples, opposing sides of a curve represent the community’s assessment. When a claim resolves, correct stakers receive rewards from the other side. A statement can be true yet attract little stake if few deem it material—a market for meaning as much as for truth.

Every interaction incurs small fees, with a portion streaming back to creators and curators. This produces continuous, on-chain remuneration for valuable data—akin to gas-paid royalties for information.

Atom and Triple registries surface the most useful identifiers and statements for users and apps alike, similar to a decentralized ranking of what matters now.

Under the hood, a directed acyclic graph models relationships, while an indexer unifies on-chain data and off-chain storage such as content-addressed file storage. Custom filters let users and developers craft personal “reality tunnels” over the same shared graph.

Helpful reviews and deep-dive posts rarely translate into portable reputation or earnings. This system reverses that: contributions accrue fractional ownership in the underlying knowledge, forming a portfolio of information assets that can grow as usage scales.

For builders, the model departs from subscription software—developers acquire Trust based on expected activity, then delegate to users to remove wallet purchase friction. The stake isn’t “spent” like gas; it fuels interactions and aligns incentives.

Because builders hold Trust, they win when their users produce widely adopted data. Instead of paying more as usage rises, they benefit from network growth and composability, changing how teams think about market cap alignment and user success.

Soft incentives matter, too. Trust circles—contextual, portable reputation layers—let you filter by people and sources you respect, encouraging high-quality contributions and reducing spam.

This shared data backbone removes the need for every team to build a database, schema, and auth. Users bring their identities, preferences, and graphs wherever they go, shrinking switching costs and improving the Web3 experience.

As agentic systems proliferate, artificial intelligence needs provenance, history, and credibility. The graph offers structured context and accountability so agents interact with reliable data rather than opaque scraps.

Trust powers all interactions. In an open network, requiring stake to create Atoms, propose Triples, or signal on claims deters low-quality noise and rewards conviction.

Core uses of the Trust token include staking to create and curate Atoms and Triples, paying the small interaction fees that fund contributor rewards, earning fee streams and fractional ownership in curated data, delegating stake to lower user friction for applications, and accumulating reputation signals that can be reused across apps through trust circles.

Backing an Atom is a public endorsement that drives it toward canonical status. On Triples, stake reflects relevance and sentiment, creating a dynamic market for ideas where tokens function as economically weighted votes.

Micro-fees from activity accumulate to contributors. As adoption grows and more apps integrate, the token’s utility expands with network usage—turning knowledge into an investable, on-chain asset class.

Because token markets change quickly, this report does not quote a live spot price or market capitalization for the Trust token. If a public market exists, the most reliable way to get the current price, market cap, and active trading venues is to check a major exchange order book or a market-data aggregator such as CoinGecko or CoinMarketCap; during beta or pre-listing phases, these figures may be unavailable or unreliable.

If the Trust token is listed for purchase, you can typically buy it through the exchanges shown in those market listings or acquire it via an on-chain swap where liquidity is available. The basic steps are: set up a supported wallet or exchange account, complete any required identity checks on centralized venues, deposit fiat or crypto, and place a spot trade; for on-chain swaps, bridge to the correct network, connect your wallet to a decentralized exchange interface, and review slippage and fees before confirming.

Rather than removing trust, the protocol surfaces it—making reputation measurable and tradable in a transparent crypto economy.

Most users won’t think about Atoms or Triples any more than they think about the underlying networking stack. They will use simple apps backed by a semantic graph that handles identity, context, and reputation under the hood.

Expect feeds curated by your trust circle, marketplaces for contextual truths, and artificial intelligence copilots that reference open, verifiable sources rather than proprietary silos. The complexity lives in the graph; the interface stays intuitive.

For developers, tapping a communal knowledge layer cuts costs and shifts competition from hoarding data to crafting better user experience. Composability accelerates iteration and reduces redundancy.

For users, preferences, follows, and reputation travel across apps. Discovery feels like an extension of real life, not a reset with every sign-up or wallet connect.

Semrush reported artificial intelligence overviews in 13.14% of Google searches in March 2025 (up from 6.49% in January), showing rapid growth toward artificial intelligence answers over links. This concentrates power, hides sources, and flattens perspectives—risky for politics, health, and finance.

The protocol counters with structured, provenance-rich data that agents can query openly. Think portable “agent cards” that make history and credibility discoverable across systems, replacing black boxes with transparent infrastructure.

The destination is a shared discovery hub where people, communities, and agents interact with portable, credible, and user-owned information.

On Base Mainnet beta, more than 244,000 participants drove 5.3 million transactions and over 5.1 million attestations—evidence that portable reputation and signal-driven curation resonate beyond theory.

The team is completing audits, migrating beta data, and stress testing at scale to smooth the path to mainnet for developers and users. Near-term milestones emphasized here include finishing those audits, completing migration from beta, and validating performance under load so developers can build on a stable, production-ready graph.

Measured rollout beats rushed launches. Behavior and incentives were validated in beta; security and scale are being addressed now so core risks are mitigated ahead of mainnet.

For crypto natives, usage trumps promises. The data indicates that when incentives align, people will contribute, stake, and curate knowledge in a way that portable identity can reuse across apps.

Millions of attestations suggest product–market fit: users want to own their data, carry reputation, and monetize expertise within an open, semantic knowledge graph.

Crypto perfected settlement; the next frontier is discovery. This protocol applies game theory and incentives to the hard problem—what to believe, who to trust, and how to coordinate truth without central gatekeepers. It unlocks a world where your identity, preferences, and reputation persist across interfaces, and where builders compete on product, not data lock-in. By turning trust into a visible, measurable primitive and knowledge into an asset that contributors can own, the system lays the groundwork for an open discovery layer for people, communities, and artificial intelligence agents.

Whether the Trust token is a good investment depends on your risk tolerance and view on adoption. Potential rewards come from increased network usage, fee flows to contributors and curators, and demand for staking signals; key risks include smart-contract or economic-design failures, regulatory uncertainty, limited liquidity, and the possibility that adoption stalls. This is not financial advice.